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Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset
Very often, the root of problems found to produce food sustainably, as well as the origin of many environmental issues, derive from making decisions with unreliable or inexistent data. Data-driven agriculture has emerged as a way to palliate the lack of meaningful information when taking critical st...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125128/ https://www.ncbi.nlm.nih.gov/pubmed/33946191 http://dx.doi.org/10.3390/s21093114 |
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author | Rovira-Más, Francisco Saiz-Rubio, Verónica Cuenca-Cuenca, Andrés |
author_facet | Rovira-Más, Francisco Saiz-Rubio, Verónica Cuenca-Cuenca, Andrés |
author_sort | Rovira-Más, Francisco |
collection | PubMed |
description | Very often, the root of problems found to produce food sustainably, as well as the origin of many environmental issues, derive from making decisions with unreliable or inexistent data. Data-driven agriculture has emerged as a way to palliate the lack of meaningful information when taking critical steps in the field. However, many decisive parameters still require manual measurements and proximity to the target, which results in the typical undersampling that impedes statistical significance and the application of AI techniques that rely on massive data. To invert this trend, and simultaneously combine crop proximity with massive sampling, a sensing architecture for automating crop scouting from ground vehicles is proposed. At present, there are no clear guidelines of how monitoring vehicles must be configured for optimally tracking crop parameters at high resolution. This paper structures the architecture for such vehicles in four subsystems, examines the most common components for each subsystem, and delves into their interactions for an efficient delivery of high-density field data from initial acquisition to final recommendation. Its main advantages rest on the real time generation of crop maps that blend the global positioning of canopy location, some of their agronomical traits, and the precise monitoring of the ambient conditions surrounding such canopies. As a use case, the envisioned architecture was embodied in an autonomous robot to automatically sort two harvesting zones of a commercial vineyard to produce two wines of dissimilar characteristics. The information contained in the maps delivered by the robot may help growers systematically apply differential harvesting, evidencing the suitability of the proposed architecture for massive monitoring and subsequent data-driven actuation. While many crop parameters still cannot be measured non-invasively, the availability of novel sensors is continually growing; to benefit from them, an efficient and trustable sensing architecture becomes indispensable. |
format | Online Article Text |
id | pubmed-8125128 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81251282021-05-17 Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset Rovira-Más, Francisco Saiz-Rubio, Verónica Cuenca-Cuenca, Andrés Sensors (Basel) Article Very often, the root of problems found to produce food sustainably, as well as the origin of many environmental issues, derive from making decisions with unreliable or inexistent data. Data-driven agriculture has emerged as a way to palliate the lack of meaningful information when taking critical steps in the field. However, many decisive parameters still require manual measurements and proximity to the target, which results in the typical undersampling that impedes statistical significance and the application of AI techniques that rely on massive data. To invert this trend, and simultaneously combine crop proximity with massive sampling, a sensing architecture for automating crop scouting from ground vehicles is proposed. At present, there are no clear guidelines of how monitoring vehicles must be configured for optimally tracking crop parameters at high resolution. This paper structures the architecture for such vehicles in four subsystems, examines the most common components for each subsystem, and delves into their interactions for an efficient delivery of high-density field data from initial acquisition to final recommendation. Its main advantages rest on the real time generation of crop maps that blend the global positioning of canopy location, some of their agronomical traits, and the precise monitoring of the ambient conditions surrounding such canopies. As a use case, the envisioned architecture was embodied in an autonomous robot to automatically sort two harvesting zones of a commercial vineyard to produce two wines of dissimilar characteristics. The information contained in the maps delivered by the robot may help growers systematically apply differential harvesting, evidencing the suitability of the proposed architecture for massive monitoring and subsequent data-driven actuation. While many crop parameters still cannot be measured non-invasively, the availability of novel sensors is continually growing; to benefit from them, an efficient and trustable sensing architecture becomes indispensable. MDPI 2021-04-30 /pmc/articles/PMC8125128/ /pubmed/33946191 http://dx.doi.org/10.3390/s21093114 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Rovira-Más, Francisco Saiz-Rubio, Verónica Cuenca-Cuenca, Andrés Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset |
title | Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset |
title_full | Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset |
title_fullStr | Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset |
title_full_unstemmed | Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset |
title_short | Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset |
title_sort | sensing architecture for terrestrial crop monitoring: harvesting data as an asset |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125128/ https://www.ncbi.nlm.nih.gov/pubmed/33946191 http://dx.doi.org/10.3390/s21093114 |
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